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                <h1 id="Redis-中的高级数据结构"><a href="#Redis-中的高级数据结构" class="headerlink" title="Redis 中的高级数据结构"></a>Redis 中的高级数据结构</h1><p>前面我们学习 Redis 中的5中基础数据类型，今天我又来了，没错，就是 Redis 中剩余的3中高级数据类型，废话不多说，我们开始吧。</p>
<h2 id="Bitmap"><a href="#Bitmap" class="headerlink" title="Bitmap"></a>Bitmap</h2><p>Bitmap 翻译过来就是位图，那它有什么作用呢？</p>
<p>我们首先想这么一个场景，二值状态统计，这个二值状态就是指集合元素的取值就只有0和1两种。比如：在签到打卡的场景中，我们只用记录签到（1）或未签到（0），这就是典型的二值状态。在我们签到统计时，每个用户的一天的签到用1个bit位就能表示，有多少天就只需要多少个bit位，不需要使用太复杂的集合类型，这时我们就可以使用 Bitmap。</p>
<p>那 Bitmap 的实现原理又是啥？</p>
<p>Bitmap 本身是用 String 类型作为底层数据结构实现的一种统计二值状态的数据类型。String 类型是会保存为二进制的字节数组，所以，Redis 就把字节数组的每个 bit 位利用起来，用来表示一个元素的二值状态，可以把 Bitmap 看作是一个 bit 数组。</p>
<p>Bitmap 提供了 GETBIT/SETBIT 操作，使用一个偏移值 offset 对 bit 数据的某一个 bit 位进行读和写，这个偏移值 offset 是从 0 开始的，最小就是0。当使用 SETBIT 对一个 bit 位进行写操作时，这个 bit 位会被设置为1。Bitmap 还提供了BITCOUNT 操作，用来统计这个 bit 数组中所有“1” 的个数。</p>
<p>那么该怎么具体使用 Bitmap 进行签到统计呢？</p>
<p>假设我们要统计 ID 3000 的用户在 2020 年 8 月份的签到情况，就可以按照下面的步骤进行操作。  </p>
<p>1）执行下面的命令，记录该用户 8 月 3 号已签到。  </p>
<pre class="line-numbers language-none"><code class="language-none">SETBIT uid:sign:3000:202008 2 1<span aria-hidden="true" class="line-numbers-rows"><span></span></span></code></pre>

<p>2）检查该用户 8 月 3 日是否签到。</p>
<pre class="line-numbers language-none"><code class="language-none">GETBIT uid:sign:3000:202008 2<span aria-hidden="true" class="line-numbers-rows"><span></span></span></code></pre>

<p>3）统计该用户在 8 月份的签到次数。</p>
<pre class="line-numbers language-none"><code class="language-none">BITCOUNT uid:sign:3000:202008<span aria-hidden="true" class="line-numbers-rows"><span></span></span></code></pre>

<p>这样，我们就知道该用户在 8 月份的签到情况了，是不是很简单呢？接下来，你可以再思考一个问题：如果记录了 1 亿个用户 10 天的签到情况，你有办法统计出这 10 天连续签到的用户总数吗？</p>
<p>在介绍具体的方法之前，我们要先知道，Bitmap 支持用 BITOP 命令对多个 Bitmap 按位做“与”“或”“异或”的操作，操作的结果会保存到一个新的 Bitmap 中。  我们用与运算举个例子：</p>
<p>假如我们对三个 Bitmap bm1、bm2、bm3对应 bit 做“与”操作，结果保存到了一个新的 Bitmap 中，这个结果 Bitmap 的 key 被设为“resmap”。</p>
<img src="https://cdn.jsdelivr.net/gh/kuangtf/PictureBed/img/image-20220327120208058.png" alt="image-20220327120208058" style="zoom:80%;" />

<p>回到上面的问题，在统计 1 亿个用户连续 10 天的签到情况时，你可以把每天的日期作为key，每个 key 对应一个 1 亿位的 Bitmap，每一个 bit 对应一个用户当天的签到情况。接下来，我们对 10 个 Bitmap 做“与”操作，得到的结果也是一个 Bitmap。在这个Bitmap 中，只有 10 天都签到的用户对应的 bit 位上的值才会是 1。最后，我们可以用BITCOUNT 统计下 Bitmap 中的 1 的个数，这就是连续签到 10 天的用户总数了。  </p>
<p>我们可以简单的算出这10天签到情况后的内存开销。每天使用 1 个 1 亿位的Bitmap，大约占 12MB 的内存（10^8/8/1024/1024），10 天的 Bitmap 的内存开销约为 120MB，内存压力不算太大。不过，在实际应用时，最好对 Bitmap 设置过期时间，让Redis 自动删除不再需要的签到记录，以节省内存开销。</p>
<p>由此可看出，在记录海量数据时，Bitmap 能有效地节省内存空间。</p>
<h2 id="HyperLogLog"><a href="#HyperLogLog" class="headerlink" title="HyperLogLog"></a>HyperLogLog</h2><p>在介绍 HyperLogLog 之前，我们先讲一下基数统计。基数统计就是指统计一个集合中不重复的元素个数。</p>
<p>以下面这个场景为例，我们来看看使用基数统计的场景：</p>
<p>比如统计网页的 UV，网页 UV 的统计有个独特的地方，就是需要去重，一个用户一天内的多次访问只能算作一次。在 Redis 的集合类型中，Set 类型默认支持去重，所以看到有去重需求时，我们可能第一时间就会想到用 Set 类型。  </p>
<p>我们看看使用 Set 的情况：有一个用户 user1 访问 page1 时，你把这个信息加到 Set 中：  </p>
<pre class="line-numbers language-none"><code class="language-none">SADD page1:uv user1<span aria-hidden="true" class="line-numbers-rows"><span></span></span></code></pre>

<p>用户 1 再来访问时，Set 的去重功能就保证了不会重复记录用户 1 的访问次数，这样，用户 1 就算是一个独立访客。当你需要统计 UV 时，可以直接用 SCARD 命令，这个命令会返回一个集合中的元素个数。</p>
<p>但是这样就没有问题了吗？如果page1 非常火爆，UV 达到了千万，这个时候，一个 Set 就要记录千万个用户ID。对于一个搞大促的电商网站而言，这样的页面可能有成千上万个，如果每个页面都用这样的一个 Set，就会消耗很大的内存空间。  </p>
<p>当然，也可以使用 Hash 类型记录 UV。例如，你可以把用户 ID 作为 Hash 集合的 key，当用户访问页面时，就用HSET 命令（用于设置 Hash 集合元素的值），对这个用户 ID 记录一个值“1”，表示一个独立访客，用户 1 访问 page1 后，我们就记录为 1 个独立访客，如下所示：</p>
<pre class="line-numbers language-none"><code class="language-none">HSET page1:uv user1 1<span aria-hidden="true" class="line-numbers-rows"><span></span></span></code></pre>

<p>即使用户 1 多次访问页面，重复执行这个 HSET 命令，也只会把 user1 的值设置为 1，仍然只记为 1 个独立访客。当要统计 UV 时，我们可以用 HLEN 命令统计 Hash 集合中的所有元素个数。</p>
<p>但是，和 Set 类型相似，当页面很多时，Hash 类型也会消耗很大的内存空间。那么，有什么办法既能完成统计，还能节省内存吗？</p>
<p>于是，我们的 HyperLogLog 就登场了。</p>
<p>HyperLogLog 是一种用于统计基数的数据集合类型，它的最大优势就在于，当集合元素数量非常多时，它计算基数所需的空间总是固定的，而且还很小。  </p>
<p>在 Redis 中，每个 HyperLogLog 只需要花费 12 KB 内存，就可以计算接近 2^64 个元素的基数。你看，和元素越多就越耗费内存的 Set 和 Hash 类型相比，HyperLogLog 就非常节省空间。  </p>
<p>在统计 UV 时，你可以用 PFADD 命令（用于向 HyperLogLog 中添加新元素）把访问页面的每个用户都添加到 HyperLogLog 中。</p>
<pre class="line-numbers language-none"><code class="language-none">PFADD page1:uv user1 user2 user3 user4 user5<span aria-hidden="true" class="line-numbers-rows"><span></span></span></code></pre>

<p>接下来，就可以用 PFCOUNT 命令直接获得 page1 的UV值了，这个命令的作用就是返回 HyperLogLog 的统计结果。</p>
<pre class="line-numbers language-none"><code class="language-none">PFCOUNT page1:uv<span aria-hidden="true" class="line-numbers-rows"><span></span></span></code></pre>

<p>但是，HyperLogLog 的统计规则是基于概率完成的，所以它给出的统计结果是有一定误差的，标准误算率是 0.81%。这也就意味着，你使用HyperLogLog 统计的 UV 是 100 万，但实际的 UV 可能是 101 万。虽然误差率不算大，但是，如果你需要精确统计结果的话，最好还是继续用 Set 或 Hash 类型。</p>
<h2 id="GEO"><a href="#GEO" class="headerlink" title="GEO"></a>GEO</h2><p>我们还是以一个场景为例，来看看 GEO 是个什么东东。</p>
<p>在生活中，我们肯定用过打车软件，或摇一摇等，对于这些场景，我们肯定是需要知道自己的地理位置的，用专业点的话说就是这些功能都是基于位置信息服务（Location-Based Service，LBS）的应用。LBS 应用访问的数据是和人或物关联的一组经纬度信息，而且要能查询相邻的经纬度范围，GEO 就非常适合应用在LBS 服务的场景中，我们来看一下它的底层结构。  </p>
<p>GEO 类型的底层数据结构是用 Sorted Set 来实现的，我们以上面叫车的例子来看：</p>
<p>用 Sorted Set 来保存车辆的经纬度信息时，Sorted Set 的元素是车辆 ID，元素的权重分数是经纬度信息，如下图所示：  </p>
<img src="https://cdn.jsdelivr.net/gh/kuangtf/PictureBed/img/image-20220327120217287.png" alt="image-20220327120217287" style="zoom:80%;" />

<p>这时问题来了，Sorted Set 元素的权重分数是一个浮点数（float 类型），而一组经纬度包含的是经度和纬度两个值，是没法直接保存为一个浮点数的，那具体该怎么进行保存呢？  </p>
<p>这就要用到 GEO 类型中的 GeoHash 编码了，关于这个编码感兴趣的同学可以自己去了解，这里就不说了。</p>
<p>下面说说 GEO 类型的使用。</p>
<p>在使用 GEO 类型时，我们经常会用到两个命令，分别是 GEOADD 和 GEORADIUS。</p>
<ul>
<li>GEOADD 命令：用于把一组经纬度信息和相对应的一个 ID 记录到 GEO 类型集合中。</li>
<li>GEORADIUS 命令：会根据输入的经纬度位置，查找以这个经纬度为中心的一定范围内的其他元素。当然，我们可以自己定义这个范围。</li>
</ul>
<p>我们还是以叫车的例子来看看这两个命令的使用：</p>
<p>假设车辆 ID 是 33，经纬度位置是（116.034579，39.030452），我们可以用一个 GEO 集合保存所有车辆的经纬度，集合 key 是 cars:locations。执行下面的这个命令，就可以把 ID 号为 33 的车辆的当前经纬度位置存入 GEO 集合中：  </p>
<pre class="line-numbers language-none"><code class="language-none">GEOADD cars:locations 116.034579 39.030452 33<span aria-hidden="true" class="line-numbers-rows"><span></span></span></code></pre>

<p>当用户想要寻找自己附近的网约车时，LBS 应用就可以使用 GEORADIUS 命令。</p>
<p>例如，LBS 应用执行下面的命令时，Redis 会根据输入的用户的经纬度信息（116.054579，39.030452 ），查找以这个经纬度为中心的 5 公里内的车辆信息，并返回给 LBS 应用。当然， 你可以修改“5”这个参数，来返回更大或更小范围内的车辆信息。</p>
<pre class="line-numbers language-none"><code class="language-none">GEORADIUS cars:locations 116.054579 39.030452 5 km ASC COUNT 10<span aria-hidden="true" class="line-numbers-rows"><span></span></span></code></pre>

<p>另外，我们还可以进一步限定返回的车辆信息。比如，我们可以使用 ASC 选项，让返回的车辆信息按照距离这个中心位置从近到远的方式来排序，以方便选择最近的车辆；还可以使用 COUNT 选项，指定返回的车辆信息的数量。毕竟，5 公里范围内的车辆可能有很多，如果返回全部信息，会占用比较多的数据带宽，这个选项可以帮助控制返回的数据量，节省带宽。  </p>
<p>可以看到，使用 GEO 数据类型可以非常轻松地操作经纬度这种信息。</p>
<p>好啦，学习的时光过得总是飞快，相信你又学到了不少可以糊弄面试官的东西了。</p>
<blockquote>
<p>巨人的肩膀：</p>
<p>极客时间 Redis 核心原理与实战</p>
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